Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning
Large language model unlearning aims to suppress designated undesirable knowledge while preserving benign capabilities. Many unlearning objectives focus on suppressing undesired answers, while recent target-guided variants specify replacement behavior but still leave update locality largely unconstrained. This paper introduces \emph{Null-Space Constrained Response-Specified Unlearning} (NSRU), a projection-constrained low-rank framework for controlled LLM unlearning. NSRU uses an explicitly structured safe target response to specify the desired behavior for each forget query, while suppressing the original undesired content. To localize adaptation, NSRU estimates per-module retain subspac...
| Authors | Bocheng Ju, Jianhua Wang, Chengliang Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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